From ea387acf2fb329308935c04456feb80cdd101cf8 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 6 Sep 2026 11:32:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: hugging-quants/gemma-2-9b-it-AWQ-INT4 Source: Original Platform --- .gitattributes | 36 + README.md | 290 +++++ config.json | 47 + generation_config.json | 9 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 1059 ++++++++++++++++ special_tokens_map.json | 34 + tokenizer.json | 3 + tokenizer.model | 3 + tokenizer_config.json | 2013 ++++++++++++++++++++++++++++++ 11 files changed, 3500 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer.model create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..f970d88 --- /dev/null +++ b/README.md @@ -0,0 +1,290 @@ +--- +base_model: google/gemma-2-9b-it +license: gemma +language: +- en +library_name: transformers +pipeline_tag: text-generation +tags: +- gemma2 +- google +- autoawq +--- + +> [!IMPORTANT] +> This repository is a community-driven quantized version of the original model [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it) which is the BF16 half-precision official version released by Google. + +> [!WARNING] +> This model has been quantized using `transformers` 4.45.0, meaning that the tokenizer available in this repository won't be compatible with lower versions. Same applies for e.g. Text Generation Inference (TGI) that only installs `transformers` 4.45.0 or higher starting in v2.3.1. + +## Model Information + +Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. + +This repository contains [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it) quantized using [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) from FP16 down to INT4 using the GEMM kernels performing zero-point quantization with a group size of 128. + +## Model Usage + +> [!NOTE] +> In order to run the inference with Gemma2 9B Instruct AWQ in INT4, around 6 GiB of VRAM are needed only for loading the model checkpoint, without including the KV cache or the CUDA graphs, meaning that there should be a bit over that VRAM available. + +In order to use the current quantized model, support is offered for different solutions as `transformers`, `autoawq`, or `text-generation-inference`. + +### 🤗 Transformers + +In order to run the inference with Gemma2 9B Instruct AWQ in INT4, you need to install the following packages: + +```bash +pip install -q --upgrade "transformers>=4.45.0" accelerate +INSTALL_KERNELS=1 pip install -q git+https://github.com/casper-hansen/AutoAWQ.git@79547665bdb27768a9b392ef375776b020acbf0c +``` + +To run the inference on top of Gemma2 9B Instruct AWQ in INT4 precision, the AWQ model can be instantiated as any other causal language modeling model via `AutoModelForCausalLM` and run the inference normally. + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig + +model_id = "hugging-quants/gemma-2-9b-it-AWQ-INT4" + +quantization_config = AwqConfig( + bits=4, + fuse_max_seq_len=512, # Note: Update this as per your use-case + do_fuse=True, +) + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + device_map="auto", + quantization_config=quantization_config +) + +prompt = [ + {"role": "user", "content": "What's Deep Learning?"}, +] +inputs = tokenizer.apply_chat_template( + prompt, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt", + return_dict=True, +).to("cuda") + +outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256) +print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]) +``` + +### AutoAWQ + +In order to run the inference with Gemma2 9B Instruct AWQ in INT4, you need to install the following packages: + +```bash +pip install -q --upgrade "transformers>=4.45.0" accelerate +INSTALL_KERNELS=1 pip install -q git+https://github.com/casper-hansen/AutoAWQ.git@79547665bdb27768a9b392ef375776b020acbf0c +``` + +Alternatively, one may want to run that via `AutoAWQ` even though it's built on top of 🤗 `transformers`, which is the recommended approach instead as described above. + +```python +import torch +from awq import AutoAWQForCausalLM +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_id = "hugging-quants/gemma-2-9b-it-AWQ-INT4" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoAWQForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + device_map="auto", +) + +prompt = [ + {"role": "user", "content": "What's Deep Learning?"}, +] +inputs = tokenizer.apply_chat_template( + prompt, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt", + return_dict=True, +).to("cuda") + +outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256) +print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]) +``` + +The AutoAWQ script has been adapted from [`AutoAWQ/examples/generate.py`](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py). + +### 🤗 Text Generation Inference (TGI) + +To run the `text-generation-launcher` with Gemma2 9B Instruct AWQ in INT4 with Marlin kernels for optimized inference speed, you will need to have Docker installed (see [installation notes](https://docs.docker.com/engine/install/)). + +Then you just need to run the TGI v2.3.0 (or higher) Docker container as follows: + +```bash +docker run --gpus all --shm-size 1g -ti -p 8080:80 \ + -v hf_cache:/data \ + -e MODEL_ID=hugging-quants/gemma-2-9b-it-AWQ-INT4 \ + -e QUANTIZE=awq \ + -e MAX_INPUT_LENGTH=4000 \ + -e MAX_TOTAL_TOKENS=4096 \ + ghcr.io/huggingface/text-generation-inference:2.3.0 +``` + +> [!NOTE] +> TGI will expose different endpoints, to see all the endpoints available check [TGI OpenAPI Specification](https://huggingface.github.io/text-generation-inference/#/). + +To send request to the deployed TGI endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`: + +```bash +curl 0.0.0.0:8080/v1/chat/completions \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "tgi", + "messages": [ + { + "role": "user", + "content": "What is Deep Learning?" + } + ], + "max_tokens": 128 + }' +``` + +Or programatically via the `huggingface_hub` Python client as follows: + +```python +import os +from huggingface_hub import InferenceClient + +client = InferenceClient(base_url="http://0.0.0.0:8080", api_key="-") + +chat_completion = client.chat.completions.create( + model="hugging-quants/gemma-2-9b-it-AWQ-INT4", + messages=[ + {"role": "user", "content": "What is Deep Learning?"}, + ], + max_tokens=128, +) +``` + +Alternatively, the OpenAI Python client can also be used (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows: + +```python +import os +from openai import OpenAI + +client = OpenAI(base_url="http://0.0.0.0:8080/v1", api_key="-") + +chat_completion = client.chat.completions.create( + model="tgi", + messages=[ + {"role": "user", "content": "What is Deep Learning?"}, + ], + max_tokens=128, +) +``` + +### vLLM + +To run vLLM with Gemma2 9B Instruct AWQ in INT4, you will need to have Docker installed (see [installation notes](https://docs.docker.com/engine/install/)) and run the latest vLLM Docker container as follows: + +```bash +docker run --runtime nvidia --gpus all --ipc=host -p 8000:8000 \ + -v hf_cache:/root/.cache/huggingface \ + vllm/vllm-openai:latest \ + --model hugging-quants/gemma-2-9b-it-AWQ-INT4 \ + --max-model-len 4096 +``` + +To send request to the deployed vLLM endpoint compatible with [OpenAI OpenAPI specification](https://github.com/openai/openai-openapi) i.e. `/v1/chat/completions`: + +```bash +curl 0.0.0.0:8000/v1/chat/completions \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "hugging-quants/gemma-2-9b-it-AWQ-INT4", + "messages": [ + { + "role": "user", + "content": "What is Deep Learning?" + } + ], + "max_tokens": 128 + }' +``` + +Or programatically via the `openai` Python client (see [installation notes](https://github.com/openai/openai-python?tab=readme-ov-file#installation)) as follows: + +```python +import os +from openai import OpenAI + +client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key=os.getenv("VLLM_API_KEY", "-")) + +chat_completion = client.chat.completions.create( + model="hugging-quants/gemma-2-9b-it-AWQ-INT4", + messages=[ + {"role": "user", "content": "What is Deep Learning?"}, + ], + max_tokens=128, +) +``` + +## Quantization Reproduction + +> [!IMPORTANT] +> In order to quantize Gemma2 9B Instruct using AutoAWQ, you will need to use an instance with at least enough CPU RAM to fit the whole model i.e. ~20GiB, and an NVIDIA GPU with 16GiB of VRAM to quantize it. +> +> Additionally, you also need to accept the Gemma2 access conditions, as it is a gated model that requires accepting those first. + +In order to quantize Gemma2 9B Instruct, first install the following packages: + +```bash +pip install -q --upgrade "torch==2.3.0" "transformers>=4.45.0" accelerate +INSTALL_KERNELS=1 pip install -q git+https://github.com/casper-hansen/AutoAWQ.git@79547665bdb27768a9b392ef375776b020acbf0c +``` + +Then you need to install the `huggingface_hub` Python SDK and login to the Hugging Face Hub. + +```bash +pip install -q --upgrade huggingface_hub +huggingface-cli login +``` + +Then run the following script, adapted from [`AutoAWQ/examples/quantize.py`](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py): + +```python +from awq import AutoAWQForCausalLM +from transformers import AutoTokenizer + +model_path = "google/gemma-2-9b-it" +quant_path = "hugging-quants/gemma-2-9b-it-AWQ-INT4" +quant_config = { + "zero_point": True, + "q_group_size": 128, + "w_bit": 4, + "version": "GEMM", +} + +# Load model +model = AutoAWQForCausalLM.from_pretrained( + model_path, low_cpu_mem_usage=True, use_cache=False, +) +tokenizer = AutoTokenizer.from_pretrained(model_path) + +# Quantize +model.quantize(tokenizer, quant_config=quant_config) + +# Save quantized model +model.save_quantized(quant_path) +tokenizer.save_pretrained(quant_path) + +print(f'Model is quantized and saved at "{quant_path}"') +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..552121e --- /dev/null +++ b/config.json @@ -0,0 +1,47 @@ +{ + "_name_or_path": "hugging-quants--gemma-2-9b-it-AWQ-INT4", + "architectures": [ + "Gemma2ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "attn_logit_softcapping": 50.0, + "bos_token_id": 2, + "cache_implementation": "hybrid", + "eos_token_id": 1, + "final_logit_softcapping": 30.0, + "head_dim": 256, + "hidden_act": "gelu_pytorch_tanh", + "hidden_activation": "gelu_pytorch_tanh", + "hidden_size": 3584, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 8192, + "model_type": "gemma2", + "num_attention_heads": 16, + "num_hidden_layers": 42, + "num_key_value_heads": 8, + "pad_token_id": 0, + 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